{"id":"https://openalex.org/W3201892058","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534381","title":"EAR: An Enhanced Adversarial Regularization Approach against Membership Inference Attacks","display_name":"EAR: An Enhanced Adversarial Regularization Approach against Membership Inference Attacks","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3201892058","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534381","mag":"3201892058"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534381","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064530863","display_name":"Hongsheng Hu","orcid":"https://orcid.org/0000-0003-4455-4227"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Hongsheng Hu","raw_affiliation_strings":["The University of Auckland, Auckland, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Auckland, Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088166627","display_name":"Zoran Sal\u010di\u0107","orcid":"https://orcid.org/0000-0001-7714-9848"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Zoran Salcic","raw_affiliation_strings":["The University of Auckland, Auckland, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Auckland, Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016115576","display_name":"Gillian Dobbie","orcid":"https://orcid.org/0000-0001-7245-0367"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Gillian Dobbie","raw_affiliation_strings":["School of Computer Science, The University of Auckland, Auckland, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, The University of Auckland, Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100752631","display_name":"Yi Chen","orcid":"https://orcid.org/0000-0003-1512-1377"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Chen","raw_affiliation_strings":["School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076120553","display_name":"Xuyun Zhang","orcid":"https://orcid.org/0000-0001-7353-4159"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xuyun Zhang","raw_affiliation_strings":["Macquarie University, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Macquarie University, Sydney, Australia","institution_ids":["https://openalex.org/I99043593"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9837999939918518,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9603999853134155,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.852581262588501},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.830632746219635},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7562294006347656},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7482198476791382},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6219981908798218},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6140782833099365},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5616428852081299},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5105262994766235},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4685787856578827}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.852581262588501},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.830632746219635},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7562294006347656},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7482198476791382},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6219981908798218},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6140782833099365},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5616428852081299},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5105262994766235},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4685787856578827},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534381","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6800000071525574}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1663973292","https://openalex.org/W1811750039","https://openalex.org/W1901129140","https://openalex.org/W2009086942","https://openalex.org/W2051267297","https://openalex.org/W2095705004","https://openalex.org/W2099471712","https://openalex.org/W2109426455","https://openalex.org/W2194775991","https://openalex.org/W2473418344","https://openalex.org/W2521028896","https://openalex.org/W2535690855","https://openalex.org/W2591882872","https://openalex.org/W2593414223","https://openalex.org/W2757528734","https://openalex.org/W2786233556","https://openalex.org/W2795435272","https://openalex.org/W2884943453","https://openalex.org/W2896457183","https://openalex.org/W2897830718","https://openalex.org/W2946930197","https://openalex.org/W2951368041","https://openalex.org/W2962760235","https://openalex.org/W2963341956","https://openalex.org/W2963378725","https://openalex.org/W2963794891","https://openalex.org/W2963844355","https://openalex.org/W2967985550","https://openalex.org/W2983140679","https://openalex.org/W3013068160","https://openalex.org/W3048684575","https://openalex.org/W3164762628","https://openalex.org/W4288103499","https://openalex.org/W4288563649","https://openalex.org/W4294643831","https://openalex.org/W4320013936","https://openalex.org/W6638214083","https://openalex.org/W6639824700","https://openalex.org/W6663658149","https://openalex.org/W6674330103","https://openalex.org/W6720608135","https://openalex.org/W6744467996","https://openalex.org/W6753186527","https://openalex.org/W6755351560","https://openalex.org/W6760759230","https://openalex.org/W6763393573","https://openalex.org/W6763736615","https://openalex.org/W6764183248","https://openalex.org/W6768661048"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W4246396837","https://openalex.org/W2482350142","https://openalex.org/W3176240006","https://openalex.org/W3126451824","https://openalex.org/W2294590153","https://openalex.org/W3211393740","https://openalex.org/W3208049411","https://openalex.org/W3022908591","https://openalex.org/W2946768379"],"abstract_inverted_index":{"Membership":[0],"inference":[1,76],"attacks":[2,66,77],"on":[3,100,170,202],"a":[4,12,17,36,48,69,79,101],"machine":[5],"learning":[6],"model":[7,86,103],"aim":[8],"to":[9,28,83,126,148],"determine":[10],"whether":[11,142],"given":[13],"data":[14],"record":[15],"is":[16,56,92,98,105],"member":[18],"of":[19,58,113,152,194],"the":[20,59,84,88,107,111,136,150,179,190,195,230],"training":[21,40,89,95,110,137],"set.":[22],"They":[23],"pose":[24],"severe":[25],"privacy":[26],"risks":[27],"individuals,":[29],"e.g.,":[30],"identifying":[31],"an":[32,93,163],"individual's":[33],"participation":[34],"in":[35,50,182],"hospital's":[37],"health":[38],"analytic":[39],"set":[41],"reveals":[42],"that":[43,51,63,97,120,225],"this":[44,159],"individual":[45],"was":[46],"once":[47],"patient":[49],"hospital.":[52],"Adversarial":[53],"regularization":[54,81,166],"(AR)":[55],"one":[57],"state-of-the-art":[60],"defense":[61,220,234],"methods":[62,213],"mitigate":[64],"such":[65],"while":[67,188],"preserving":[68,189],"model's":[70],"prediction":[71,192],"accuracy.":[72],"AR":[73,153,181],"adds":[74],"membership":[75],"as":[78,109],"new":[80,176,227],"term":[82],"target":[85,207],"during":[87,135],"process.":[90],"It":[91],"adversarial":[94,115,165],"algorithm":[96],"trained":[99],"defended":[102],"which":[104],"essentially":[106],"same":[108,191],"generator":[112],"generative":[114],"networks":[116],"(GANs).":[117],"We":[118,198,222],"observe":[119],"many":[121],"GAN":[122,144],"variants":[123,145],"are":[124,146],"able":[125],"generate":[127],"higher":[128],"quality":[129],"samples":[130],"and":[131,214],"offer":[132],"more":[133,184],"stability":[134],"phase":[138],"than":[139],"GANs.":[140],"However,":[141],"these":[143],"available":[147],"improve":[149],"effectiveness":[151],"has":[154],"not":[155],"been":[156],"investigated.":[157],"In":[158],"paper,":[160],"we":[161],"propose":[162],"enhanced":[164],"(EAR)":[167],"method":[168,228],"based":[169],"Least":[171],"Square":[172],"GANs":[173],"(LSGANs).":[174],"The":[175],"EAR":[177,201],"surpasses":[178],"existing":[180],"offering":[183],"powerful":[185],"defensive":[186],"ability":[187],"accuracy":[193],"protected":[196],"classifiers.":[197],"systematically":[199],"evaluate":[200],"five":[203],"datasets":[204],"with":[205,217],"different":[206,211],"classifiers":[208],"under":[209],"four":[210,218],"attack":[212],"compare":[215],"it":[216],"other":[219,233],"methods.":[221,235],"experimentally":[223],"show":[224],"our":[226],"performs":[229],"best":[231],"among":[232]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}